Anti-BIRC5 autoantibody serves as a valuable biomarker for diagnosing AFP-negative hepatocellular carcinoma.

Background: Autoantibodies targeting tumor-associated antigens (TAAbs) have emerged as promising biomarkers for early cancer detection. This research aimed to assess the diagnostic capacity of anti-BIRC5 autoantibody in detecting AFP-negative hepatocellular carcinoma (ANHCC). Methods: This research was carried out in three stages (discovery phase, validation phase, and evaluation phase) and included a total of 744 participants. Firstly, the anti-BIRC5 autoantibody was discovered using protein microarray, exhibiting a higher positive rate in ANHCC samples (ANHCCs) compared to normal control samples (NCs). Secondly, the anti-BIRC5 autoantibody was validated through enzyme-linked immunosorbent assay (ELISA) in 85 ANHCCs and 85 NCs from two clinical centers (Zhengzhou and Nanchang). Lastly, the diagnostic usefulness of the anti-BIRC5 autoantibody for hepatocellular carcinoma (HCC) was evaluated by ELISA in a cohort consisting of an additional 149 AFP-positive hepatocellular carcinoma samples (APHCCs), 95 ANHCCs and 244 NCs. The association of elevated autoantibody to high expression of BIRC5 in HCC was further explored by the database from prognosis, immune infiltration, DNA methylation, and gene mutation level. Results: In the validation phase, the area under the ROC curve (AUC) of anti-BIRC5 autoantibody to distinguish ANHCCs from NCs in Zhengzhou and Nanchang centers was 0.733 and 0.745, respectively. In the evaluation phase, the AUCs of anti-BIRC5 autoantibody for identifying ANHCCs and HCCs from NCs were 0.738 and 0.726, respectively. Furthermore, when combined with AFP, the AUC for identifying HCCs from NCs increased to 0.914 with a sensitivity of 77.5% and specificity of 91.8%. High expression of BIRC5 gene is not only correlated with poor prognosis of HCCs, but also significantly associated with infiltration of immune cells, DNA methylation, and gene mutation. Conclusion: The findings suggest that the anti-BIRC5 autoantibody could serve as a potential biomarker for ANHCC, in addition to its supplementary role alongside AFP in the diagnosis of HCC. Next, we can carry out specific verification and explore the function of anti-BIRC5 autoantibody in the occurrence and development of HCC.

CRET-based immunoassay on magnetic beads for selective and sensitive detection of Nanog antigen as a key cancer stem cell marker.

A method is presented for chemiluminescence resonance energy transfer (CRET) using APTES-Fe3O4 as a highly efficient energy acceptor with strong magnetic effectiveness over extended distances, while an Au@BSA-luminol composite acts as the donor. In order to boost the chemiluminescence reactions, CuO nanoparticles were successfully employed. The distance between the donor and acceptor is a crucial factor in the occurrence of the CRET phenomenon. A sensitive and high-throughput sandwich chemiluminescence immunosensor has been developed accordingly with a linear range of 1.0 × 10-7 g/L to 6.0 × 10-5 g/L and a limit of detection of 0.8 × 10-7 g/L. The CRET-based sandwich immunosensor has the potential to be implemented to early cancer diagnosis because of its high sensitivity in detecting Nanog, fast analysis (30 min), and simplicity. Furthermore, this approach has the potential to be adapted for the recognition of other antigen-antibody immune complexes by utilizing the corresponding antigens and their selective antibodies.

Development of a Risk Model and Genotyping Patterns Based on Disulfidptosis-Related lncRNAs to Predict Prognosis and Immune Landscape in Osteosarcoma.

BACKGROUND: Osteosarcoma (OS) is the most prevalent orthopedic malignancy with a dismal prognosis. Disulfidptosis-related lncRNAs (DRLncs) may be related to the progression of OS, but their potential molecular regulatory role is still unclear. METHODS: Based on the data collected from The Cancer Genome Atlas (TCGA), we conducted correlation analysis and the univariate Cox analysis to screen prognosis-related DRLncs, followed by developing genotyping patterns and corresponding classifier. Subsequently, the survival analysis, enrichment analysis, drug sensitivity analysis and immune infiltration analysis were performed. Afterward, multivariate Cox regression was used to construct a risk model, which was further validated by the receiver operating characteristic (ROC) curve. The aberrant expression of hub DRLncs in OS was validated using the Reverse Transcription Polymerase Chain Reaction (RT-qPCR) assay. RESULTS: We identified 262 DRLncs and eleven prognosis-related DRLncs through filtering. We then constructed two distinct expression patterns of prognosis-related DRLncs and developed a classifier. We obtained 393 differentially expressed genes (DEGs) between different subtypes, which were significantly enriched in biological processes related to the extracellular matrix, integrin binding, focal adhesion, and Wnt signaling pathways. Through immune infiltration analysis, the activated CD4 memory T cells, resting natural killer (NK) cells, M1 macrophages, and resting dendritic cells (DC) were observed to exhibit different abundance in distinct subtypes. In the drug sensitivity analysis, tamoxifen showed a promising effect for drug-resistant OS. Furthermore, we identified five hub DRLncs and constructed a risk model. The RT-qPCR confirmed the aberrant expression of five hub DRLncs in OS. CONCLUSIONS: The present study identified DRLncs in OS, and conducted a comprehensive investigation of DRLncs-related expression patterns, survival status, immune landscape and drug sensitivity to reveal the difference in prognostic, pharmacological and immunological phenotype characteristics between distinct subtypes. Additionally, we developed a risk model to predict the prognosis, and constructed a genotyping classifier to predict the above phenotype characteristics in OS.

Staining pattern of specific and cross-reacting Melan-A antibodies: A comparative study on 15,840 samples from 133 human tumor types.

The Melan-A (melanocyte antigen) protein, also termed 'melanoma antigen recognized by T cells 1' (MART-1) is a protein with unknown function whose expression is specific for the melanocyte lineage. Antibodies against Melan-A are thus used for identifying melanocytic tumors, but some Melan-A antibodies show an additional - diagnostically useful - cross-reactivity against an unspecified protein involved in corticosteroid hormone synthesis. To comprehensively compare the staining patterns of a specific and a cross-reactive Melan-A antibody in normal and neoplastic tissues, tissue microarrays containing 15,840 samples from 133 different tumor types and subtypes as well as 608 samples of 76 different normal tissue types were analyzed by immunohistochemistry. For the Melan-A-specific antibody 'Melan-A specific' (MSVA-900M), Melan-A positivity was seen in 96.0% of 25 benign nevi, 93.0% of 40 primary and 86.7% of 75 metastatic melanomas, 82.4% of 85 renal angiomyolipomas as well as 96.4% of 84 neurofibromas, 2.2% of 46 granular cell tumors, 1.0% of 104 schwannomas, and 1.1% of 87 leiomyosarcomas. The cross-reactive antibody 'Melan-A+' (MSVA-901M+) stained 98.1% of the tumors stained by 'Melan-A specific'. In addition, high positivity rates were seen in sex-cord-stroma tumors of the ovary (35.3%-100%) and the testis (86.7%) as well as for adrenocortical neoplasms (76.3%-83.0%). Only nine further tumor groups showed Melan-A+ staining, including five different categories of urothelial carcinomas. Our data provide a comprehensive overview on the staining patterns of specific and cross-reactive Melan-A antibodies. The data demonstrate that both antibodies are highly useful for their specific purpose. It is important for pathologists to distinguish these two Melan-A antibody subtypes for their daily work.

Unraveling pancreatic ductal adenocarcinoma immune prognostic signature through a naive B cell gene set.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC), a leading cause of cancer mortality, has a complex pathogenesis involving various immune cells, including B cells and their subpopulations. Despite emerging research on the role of these cells within the tumor microenvironment (TME), the detailed molecular interactions with tumor-infiltrating immune cells (TIICs) are not fully understood. METHODS: We applied CIBERSORT to quantify TIICs and naive B cells, which are prognostic for PDAC. Marker genes from scRNA-seq and modular genes from weighted gene co-expression network analysis (WGCNA) were integrated to identify naive B cell-related genes. A prognostic signature was constructed utilizing ten machine-learning algorithms, with validation in external cohorts. We further assessed the immune cell diversity, ESTIMATE scores, and immune checkpoint genes (ICGs) between patient groups stratified by risk to clarify the immune landscape in PDAC. RESULTS: Our analysis identified 994 naive B cell-related genes across single-cell and bulk transcriptomes, with 247 linked to overall survival. We developed a 12-gene prognostic signature using Lasso and plsRcox algorithms, which was confirmed by 10-fold cross-validation and showed robust predictive power in training and real-world cohorts. Notably, we observed substantial differences in immune infiltration between patients with high and low risk. CONCLUSION: Our study presents a robust prognostic signature that effectively maps the complex immune interactions in PDAC, emphasizing the critical function of naive B cells and suggesting new avenues for immunotherapeutic interventions. This signature has potential clinical applications in personalizing PDAC treatment, enhancing the understanding of immune dynamics, and guiding immunotherapy strategies.

Autoantibodies against Endophilin A2 as a novel biomarker are beneficial to early diagnosis of breast cancer.

BACKGROUND: Due to the lack of early symptoms, breast cancer is frequently overlooked, leading to distant metastases and multi-organ lesions that directly threaten patients' lives. We have identified a novel tumor marker, antibodies to endophilin A2 (EA2), to improve early diagnosis of breast cancer. METHODS: Antibody levels of EA2 were analyzed in sera of patients with cancers of different origins and stages by indirect enzyme-linked immunosorbent assay (ELISA). Diagnostic accuracy and reference range were determined by the area under the receiver operating curve and distribution curve. The levels of EA2 antigen in sera were determined by sandwich ELISA. RESULTS: The levels of antibodies against EA2 were higher in sera of patients with breast cancer (P < 0.0001), liver cancer (P = 0.0005), gastric cancer (P = 0.0026), and colon cancer (P = 0.0349) than those in healthy controls, but not in patients with rectal cancer (P = 0.1151), leukemia (P = 0.7508), or lung cancer (P = 0.2247). The highest diagnostic value was for breast cancer, particularly in early cases (AUC = 0.8014) and those with distant metastases (AUC = 0.7885). The titers of EA2 antibodies in sera were correlated with levels of EA2 antigen in breast cancer patients. CONCLUSION: Antibodies to EA2 are novel blood biomarkers for early diagnosis of breast cancer that warrants further study in larger-scale cohort studies.

TRBC1 in flow cytometry: Assay development, validation, and reporting considerations.

The assessment of T-cell clonality by flow cytometry has long been suboptimal, relying on aberrant marker expression and/or intensity. The introduction of TRBC1 shows much promise for improving the diagnosis of T-cell neoplasms in the clinical flow laboratory. Most laboratories considering this marker already have existing panels designed for T-cell workups and will be determining how best to incorporate TRBC1. We present this comprehensive summary of TRBC1 and supplemental case examples to familiarize the flow cytometry community with its potential for routine application, provide examples of how to incorporate it into T-cell panels, and signal caution in interpreting the results in certain diagnostic scenarios where appropriate.

Comprehensive peripheral blood immunoprofiling reveals five immunotypes with immunotherapy response characteristics in patients with cancer.

The lack of comprehensive diagnostics and consensus analytical models for evaluating the status of a patient's immune system has hindered a wider adoption of immunoprofiling for treatment monitoring and response prediction in cancer patients. To address this unmet need, we developed an immunoprofiling platform that uses multiparameter flow cytometry to characterize immune cell heterogeneity in the peripheral blood of healthy donors and patients with advanced cancers. Using unsupervised clustering, we identified five immunotypes with unique distributions of different cell types and gene expression profiles. An independent analysis of 17,800 open-source transcriptomes with the same approach corroborated these findings. Continuous immunotype-based signature scores were developed to correlate systemic immunity with patient responses to different cancer treatments, including immunotherapy, prognostically and predictively. Our approach and findings illustrate the potential utility of a simple blood test as a flexible tool for stratifying cancer patients into therapy response groups based on systemic immunoprofiling.

Comprehensive analysis reveals that LTBR is a immune-related biomarker for glioma.

Glioma is a common malignant brain tumor with great heterogeneity and huge difference in clinical outcomes. Although lymphotoxin (LT) beta receptor (LTBR) has been linked to immune system and response development for decades, the expression and function in glioma have not been investigated. To confirm the expression profile of LTBR, integrated RNA-seq data from glioma and normal brain tissues were analyzed. Functional enrichment analysis, TMEscore analysis, immune infiltration, the correlation of LTBR with immune checkpoints and ferroptosis, and scRNAseq data analysis in gliomas were in turn performed, which pointed out that LTBR was pertinent to immune functions of macrophages in gliomas. In addition, after being trained and validated in the tissue samples of the integrated dataset, an LTBR DNA methylation-based prediction model succeeded to distinguish gliomas from non-gliomas, as well as the grades of glioma. Moreover, by virtue of the candidate LTBR CpG sites, a prognostic risk-score model was finally constructed to guide the chemotherapy, radiotherapy, and immunotherapy for glioma patients. Taken together, LTBR is closely correlated with immune functions in gliomas, and LTBR DNA methylation could serve as a biomarker for diagnosis and prognosis of gliomas.

Serum anti-CFL1, anti-EZR, and anti-CYPA autoantibody as diagnostic markers in ovarian cancer.

The purpose of this study was to identify novel autoantibodies against tumor-associated antigens (TAAs) and explore a diagnostic panel for Ovarian cancer (OC). Enzyme-linked immunosorbent assay was used to detect the expression of five anti-TAA autoantibodies in the discovery (70 OC and 70 normal controls) and validation cohorts (128 OC and 128 normal controls). Machine learning methods were used to construct a diagnostic panel. Serum samples from 81 patients with benign ovarian disease were used to identify the specificity of anti-TAA autoantibodies for OC. In both the discovery and validation cohorts, the expression of anti-CFL1, anti-EZR, anti-CYPA, and anti-PFN1 was higher in patients with OC than that in normal controls. The area under the receiver operating characteristic curve, sensitivity, and specificity of the panel containing anti-CFL1, anti-EZR, and anti-CYPA were 0.762, 55.56%, and 81.31%. The panel identified 53.06%, 53.33%, and 51.11% of CA125 negative, HE4 negative and the Risk of Ovarian Malignancy Algorithm negative OC patients, respectively. The combination of the three anti-TAA autoantibodies can serve as a favorable diagnostic tool for OC and has the potential to be a complementary biomarker for CA125 and HE4 in the diagnosis of ovarian cancer.

Construction of an immunosensor based on Cys/Au@TiO2 modification for the detection of liver cancer marker PIVKA-II.

An ultrasensitive immunosensor of Cys/Au@TiO2 based on disposable screen-printed electrodes (SPE) for PIVKA-II detection for hepatocellular carcinoma (HCC) diagnosis was developed by utilizing Cystine (Cys) and nanocomposite Au@TiO2. Firstly, HAuCl4 underwent a reduction reaction with NaBH4, then Au nanoparticles were coated onto TiO2 nanoparticles. Followed, Cys/Au@TiO2 was formed through self-assembly of cysteine to allow the monoclonal antibody of abnormal thrombospondin to bound to the amino group on the surface of the composite by covalent bonding. The mechanism is to determine the changes in the current of the sensor caused by the specific binding of the abnormal prothrombin monoclonal antibody adsorbed by the complex with its antigen. The Cys/Au@TiO2 immunosensor was fully characterized by various analytical approaches and it showed a wide linear testing range of 1-10000 pg mL-1 (R2 = 0.991) and the limit of detection down to 0.77 pg ml-1, with highly sensitivity and specificity. The results showed that the developed immunosensor platform can effectively detect trace amounts of PIVKA-II protein and has potent clinical application for HCC diagnosis.

Combination of an Autoantibody Panel and Alpha-Fetoprotein for Early Detection of Hepatitis B Virus-Associated Hepatocellular Carcinoma.

The purpose of this study was to identify biomarkers associated with hepatitis B virus-associated hepatocellular carcinoma (HBV-HCC) and to develop a new combination with good diagnostic performance. This study was divided into four phases: discovery, verification, validation, and modeling. A total of four candidate tumor-associated autoantibodies (TAAb; anti-ZIC2, anti-PCNA, anti-CDC37L1, and anti-DUSP6) were identified by human proteome microarray (52 samples) and bioinformatics analysis. Subsequently, these candidate TAAbs were further confirmed by indirect ELISA with two testing cohorts (120 samples for verification and 663 samples for validation). The AUC for these four TAAbs to identify patients with HBV-HCC from chronic hepatitis B (CHB) patients ranged from 0.693 to 0.739. Finally, a diagnostic panel with three TAAbs (anti-ZIC2, anti-CDC37L1, and anti-DUSP6) was developed. This panel showed superior diagnostic efficiency in identifying early HBV-HCC compared with alpha-fetoprotein (AFP), with an AUC of 0.834 [95% confidence interval (CI), 0.772-0.897] for this panel and 0.727 (95% CI, 0.642-0.812) for AFP (P = 0.0359). In addition, the AUC for this panel to identify AFP-negative patients with HBV-HCC was 0.796 (95% CI, 0.734-0.858), with a sensitivity of 52.4% and a specificity of 89.0%. Importantly, the panel in combination with AFP significantly increased the positive rate for early HBV-HCC to 84.1% (P = 0.005) and for late HBV-HCC to 96.3% (P < 0.001). Our findings suggest that AFP and the autoantibody panel may be independent but complementary serologic biomarkers for HBV-HCC detection. PREVENTION RELEVANCE: We developed a robust diagnostic panel for identifying patients with HBV-HCC from patients with CHB. This autoantibody panel provided superior diagnostic performance for HBV-HCC at an early stage and/or with negative AFP results. Our findings suggest that AFP and the autoantibody panel may be independent but complementary biomarkers for HBV-HCC detection.

Exploratory pilot study to characterize the immune landscapes of malignant pleural effusions and their corresponding primary tumors from patients with breast carcinoma and lung adenocarcinoma.

INTRODUCTION: Malignant pleural effusion (MPE) is a frequent complication of advanced malignancies. In this pilot study, we characterized the immune landscapes of MPEs, compared them to their primary tumor (PT) samples from breast carcinoma (BC) and lung adenocarcinoma (LADC), and tested the utility of multiplexed image technology in cytological samples. MATERIALS AND METHODS: We evaluated the immune contexture of 6 BC and 5 LADC MPEs and their PTs using 3 multiplex immunofluorescence panels. We explored the associations between sample characteristics and pleural effusion-free survival. RESULTS: No MPE samples had positive programmed death-ligand 1 expression in malignant cells, although 3 of 11 PTs has positive programmed death-ligand 1 expression (more than 1% expression in malignant cells). Overall, in LADC samples, cluster of differentiation 3 (CD3)+ T cells and CD3+CD8+ cytotoxic T cells predominated (median percentages for MPEs versus PTs: 45.6% versus 40.7% and 4.7% versus 6.6%, respectively) compared with BC. CD68+ macrophages predominated in the BC samples (medians for MPEs 61.2% versus PTs for 57.1%) but not in the LADC samples. Generally in PTs, CD3+CD8+ forkhead box P3+ T cells and the median distances from the malignant cells to CD3+CD8+Ki67+ and CD3+ programmed cell death protein 1 + T cells correlated to earlier MPE after PT diagnosis. CONCLUSIONS: The immune cell phenotypes in the MPEs and PTs were similar within each cancer type but different between BC versus LADC. An MPE analysis can potentially be used as a substitute for a PT analysis, but an expanded study of this topic is essential.

The Deep Learning Framework iCanTCR Enables Early Cancer Detection Using the T-cell Receptor Repertoire in Peripheral Blood.

T cells recognize tumor antigens and initiate an anticancer immune response in the very early stages of tumor development, and the antigen specificity of T cells is determined by the T-cell receptor (TCR). Therefore, monitoring changes in the TCR repertoire in peripheral blood may offer a strategy to detect various cancers at a relatively early stage. Here, we developed the deep learning framework iCanTCR to identify patients with cancer based on the TCR repertoire. The iCanTCR framework uses TCRß sequences from an individual as an input and outputs the predicted cancer probability. The model was trained on over 2,000 publicly available TCR repertoires from 11 types of cancer and healthy controls. Analysis of several additional publicly available datasets validated the ability of iCanTCR to distinguish patients with cancer from noncancer individuals and demonstrated the capability of iCanTCR for the accurate classification of multiple cancers. Importantly, iCanTCR precisely identified individuals with early-stage cancer with an AUC of 86%. Altogether, this work provides a liquid biopsy approach to capture immune signals from peripheral blood for noninvasive cancer diagnosis. SIGNIFICANCE: Development of a deep learning-based method for multicancer detection using the TCR repertoire in the peripheral blood establishes the potential of evaluating circulating immune signals for noninvasive early cancer detection.

The anti-cancer immune response in breast cancer: current and emerging biomarkers and treatments.

Triple-negative breast cancers (TNBCs) exhibit heightened T cell infiltration, contributing to an enhanced response to immune checkpoint blockade (ICB) compared with other subtypes. An immune-rich immune microenvironment correlates with improved prognosis in early and advanced TNBC. Combination chemotherapy and ICB is now the standard of care in early- and late-stage TNBC. Although programmed death ligand-1 (PD-L1) positivity predicts ICB response in advanced stages, its role in early-stage disease remains uncertain. Despite neoadjuvant ICB becoming common in early-stage TNBC, the necessity of adjuvant ICB after surgery remains unclear. Understanding the molecular basis of the immune response in breast cancer is vital for precise biomarkers for ICB and effective combination therapy strategies.

Overexpression of SH2D1A promotes cancer progression and is associated with immune cell infiltration in hepatocellular carcinoma via bioinformatics and in vitro study.

BACKGROUND: SH2 domain containing 1A (SH2D1A) expression has been linked to cancer progression. However, the functions of SH2D1A in hepatocellular carcinoma (HCC) have not been reported. METHODS: The effects of SH2D1A on the proliferation, migration, and invasion of HCC cells and the related pathways were re-explored in cell models with SH2D1A overexpression using the CCK-8, migration and invasion assays and western blotting. The functions and mechanisms of genes co-expressed with SH2D1A were analyzed using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The relationship between SH2D1A expression and immune microenvironment features in HCC was explored. RESULTS: Elevated SH2D1A expression promoted cell proliferation, migration, and invasion, which was related to the overexpression of p-Nf-κB and BCL2A1 protein levels in HCC. SH2D1A expression was related to the immune, stromal, and ESTIMATE scores, and the abundance of immune cells, such as B cells, CD8+ T cells, and T cells. SH2D1A expression was significantly related to the expression of immune cell markers, such as PDCD1, CD8A, and CTLA4 in HCC. CONCLUSION: SH2D1A overexpression was found to promote cell growth and metastasis via the Nf-κB signaling pathway and may be related to the immune microenvironment in HCC. The findings indicate that SH2D1A can function as a biomarker in HCC.

FCGR2B as a prognostic and immune microenvironmental marker for gliomas based on transcriptomic analysis.

To explore the expression and prognosis of Fc fragment of IgG low affinity IIb receptor (FCGR2B) in glioma and its relationship with immune microenvironment, so as to provide potential molecular targets for the treatment of glioma. We analyzed the gene expression of FCGR2B using the Cancer Genome Atlas database, Chinese Glioma Genome Atlas, Gene Expression Omnibus database and other glioma related databases. Moreover, we generated survival receiver operating characteristic curve, carried out univariate and multivariate Cox analysis and nomograph construction, and analyzed the relationship between FCGR2B and prognosis. According to the median of FCGR2B gene expression value, the differential expression analysis was carried out by high and low grouping method, and the gene ontology, Kyoto encyclopedia of genes and genomes, and gene set enrichment analysis enrichment analysis were carried out to explore the possible mechanism. Then, the correlation between immune score of glioma and prognosis, World Health Organization grade and FCGR2B expression was analyzed. Finally, the correlation between FCGR2B expression and the proportion of tumor infiltrating immune cells, immune checkpoints, tumor mutation load and immune function was analyzed. The expression of FCGR2B in gliomas was higher than that in normal tissues and was associated with poor prognosis. Independent prognostic analysis showed that FCGR2B was an independent prognostic factor for glioma. The analysis of gene ontology and gene set enrichment analysis showed that FCGR2B was closely related to immune-related functions. The analysis of immune scores and prognosis, World Health Organization grade and FCGR2B expression in gliomas indicated that patients with high immune scores had significantly poorer overall survival and higher tumor pathological grade. In addition, immune scores were significantly positively correlated with the expression of FCGR2B. The analysis of tumor infiltrating immune cells suggested that the expression level of FCGR2B affected the immune activity of TME. In addition, the expression of FCGR2B was positively correlated with almost all immune checkpoint molecules including CD28, CD44, TNFSF14, PDCD1LG2, LAIR1, and CD48 and was significantly positively correlated with tumor mutation load. All immunobiological functions of the high expression group of FCGR2B were significantly inhibited. FCGR2B may play an important role in the occurrence, development and invasion of tumor by influencing the tumor microenvironment of immunosuppression. FCGR2B may be an important target for the treatment of glioma.

PLEKHA4 is Associated with Tumour Microenvironment, Stemness, Proliferation and Poor Prognosis of Gliomas.

BACKGROUND: Glioma is the most common intracranial malignancy. Immune-infiltration and tumour stemness are associated with the prognosis of glioma. Although pleckstrin homology containing family A, number 4 (PLEKHA4) is widely expressed in various human cancers, its role in glioma remains unclear. METHODS: We examined the features and clinical significance of PLEKHA4 in gliomas by analysing relevant data from the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) databases. Gene set enrichment analysis (GSEA) was performed to determine the possible functions and pathways involving PLEKHA4 in glioma. The relationship between PLEKHA4 expression and the degree of oncogenic dedifferentiation was analysed using stemness scores (ss) calculated from epigenetic and transcriptomic features. We also explored the relationship between PLEKHA4 expression and immune cell infiltration in gliomas using the CIBERSORT databases. Furthermore, drug sensitivity analysis was performed using datasets from the GDSC and GTRP databases. In addition, we performed relevant in vitro experimental studies. RESULTS: PLEKHA4 DNA hypomethylation status was associated with its high expression in glioma tissues as well as poor prognoses. Univariate and multivariate Cox analyses indicated that PLEKHA4 expression may be considered as an independent prognostic factor in patients with glioma. GSEA indicated that high PLEKHA4 expression was associated with Janus kinase (JAK)/signal transducer and activator of transcription (STAT), Wingless-Type MMTV Integration Site Family (Wnt), JUN N-terminal kinase (JNK) signalling pathways and involved in apoptotic, cytoskeletal, and cell adhesion biological processes (BPs). In addition, increased PLEKHA4 expression was associated with higher glioma stemness scores than lower PLEKHA4 expression levels. Furthermore, the expression of PLEKHA4 was shown to be associated with glioma infiltration by CD4+ T cells, B cells, neutrophils, macrophages, and dendritic cells. Drug sensitivity analysis also showed that PLEKHA4 expression was negatively correlated with the sensitivity of several small molecule kinase inhibitors. Furthermore, in vitro experiments confirmed that PLEKHA4 knockdown inhibited the proliferation of glioma cells. CONCLUSIONS: PLEKHA4 is highly expressed in glioma tissues and correlated with tumour stemness, immune cell infiltration and proliferation, suggesting its potential as a novel prognostic biomarker and therapeutic target in glioma.

Variable Intrinsic Expression of Immunoregulatory Biomarkers in Breast Cancer Cell Lines, Mammospheres, and Co-Cultures.

Advances in immunotherapy have increased interest in knowing the role of the immune system in breast cancer (BC) pathogenesis. Therefore, immune checkpoints (IC) and other pathways related to immune regulation, such as JAK2 and FoXO1, have emerged as potential targets for BC treatment. However, their intrinsic gene expression in vitro has not been extensively studied in this neoplasia. Thus, we evaluated the mRNA expression of tumor-cell-intrinsic CTLA-4, PDCD1 (PD1), CD274 (PD-L1), PDCD1LG2 (PD-L2), CD276 (B7-H3), JAK2, and FoXO1 in different BC cell lines, derived mammospheres, and co-cultures with peripheral blood mononuclear cells (PBMCs) by real-time quantitative polymerase chain reaction (qRT-PCR). Our results showed that intrinsic CTLA-4, CD274 (PD-L1), and PDCD1LG2 (PD-L2) were highly expressed in triple-negative cell lines, while CD276 was predominantly overexpressed in luminal cell lines. In contrast, JAK2 and FoXO1 were under-expressed. Moreover, high levels of CTLA-4, PDCD1 (PD1), CD274 (PD-L1), PDCD1LG2 (PD-L2), and JAK2 were found after mammosphere formation. Finally, the interaction between BC cell lines and peripheral blood mononuclear cells (PBMCs) stimulates the intrinsic expression of CTLA-4, PCDC1 (PD1), CD274 (PD-L1), and PDCD1LG2 (PD-L2). In conclusion, the intrinsic expression of immunoregulatory genes seems very dynamic, depending on BC phenotype, culture conditions, and tumor-immune cell interactions.

Prognostic and Predictive Value of Immune-Related Gene Expression Signatures vs Tumor-Infiltrating Lymphocytes in Early-Stage ERBB2/HER2-Positive Breast Cancer: A Correlative Analysis of the CALGB 40601 and PAMELA Trials.

Importance: Both tumor-infiltrating lymphocytes (TILs) assessment and immune-related gene expression signatures by RNA profiling predict higher pathologic complete response (pCR) and improved event-free survival (EFS) in patients with early-stage ERBB2/HER2-positive breast cancer. However, whether these 2 measures of immune activation provide similar or additive prognostic value is not known. Objective: To examine the prognostic ability of TILs and immune-related gene expression signatures, alone and in combination, to predict pCR and EFS in patients with early-stage ERBB2/HER2-positive breast cancer treated in 2 clinical trials. Design, Setting, and Participants: In this prognostic study, a correlative analysis was performed on the Cancer and Leukemia Group B (CALGB) 40601 trial and the PAMELA trial. In the CALGB 40601 trial, 305 patients were randomly assigned to weekly paclitaxel with trastuzumab, lapatinib, or both for 16 weeks. The primary end point was pCR, with a secondary end point of EFS. In the PAMELA trial, 151 patients received neoadjuvant treatment with trastuzumab and lapatinib for 18 weeks. The primary end point was the ability of the HER2-enriched subtype to predict pCR. The studies were conducted from October 2013 to November 2015 (PAMELA) and from December 2008 to February 2012 (CALGB 40601). Data analyses were performed from June 1, 2020, to January 1, 2022. Main Outcomes and Measures: Immune-related gene expression profiling by RNA sequencing and TILs were assessed on 230 CALGB 40601 trial pretreatment tumors and 138 PAMELA trial pretreatment tumors. The association of these biomarkers with pCR (CALGB 40601 and PAMELA) and EFS (CALGB 40601) was studied by logistic regression and Cox analyses. Results: The median age of the patients was 50 years (IQR, 42-50 years), and 305 (100%) were women. Of 202 immune signatures tested, 166 (82.2%) were significantly correlated with TILs. In both trials combined, TILs were significantly associated with pCR (odds ratio, 1.01; 95% CI, 1.01-1.02; P = .02). In addition to TILs, 36 immune signatures were significantly associated with higher pCR rates. Seven of these signatures outperformed TILs for predicting pCR, 6 of which were B-cell related. In a multivariable Cox model adjusted for clinicopathologic factors, including PAM50 intrinsic tumor subtype, the immunoglobulin G signature, but not TILs, was independently associated with EFS (immunoglobulin G signature-adjusted hazard ratio, 0.63; 95% CI, 0.42-0.93; P = .02; TIL-adjusted hazard ratio, 1.00; 95% CI, 0.98-1.02; P = .99). Conclusions and Relevance: Results of this study suggest that multiple B-cell-related signatures were more strongly associated with pCR and EFS than TILs, which largely represent T cells. When both TILs and gene expression are available, the prognostic value of immune-related signatures appears to be superior.